Condition Monitoring and Control for Intelligent Manufacturing

Condition Monitoring and Control for Intelligent Manufacturing
اسم المؤلف
Lihui Wang and Robert X. Gao
التاريخ
25 مارس 2023
المشاهدات
275
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(لا توجد تقييمات)
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Condition Monitoring and Control for Intelligent Manufacturing
Lihui Wang and Robert X. Gao (Eds.)
Contents
List of Contributors xvii
1 Monitoring and Control of Machining 1
A. Galip Ulsoy
1.1 Introduction . 1
1.2 Machining Processes . 6
1.3 Monitoring 10
1.3.1 Tool Failure 10
1.3.2 Tool Wear . 12
1.4 Servo Control 15
1.5 Process Control . 17
1.6 Supervisory Control 23
1.7 Concluding Remarks . 25
References . 27
2 Precision Manufacturing Process Monitoring
with Acoustic Emission . 33
D.E. Lee, Inkil Hwang, C.M.O. Valente, J.F.G. Oliveira
and David A. Dornfeld
2.1 Introduction . 33
2.2 Requirements for Sensor Technology at the Precision Scale 35
2.3 Sources of AE in Precision Manufacturing . 37
2.4 AE-based Monitoring of Grinding Wheel Dressing 39
2.4.1 Fast AE RMS Analysis for Wheel Condition Monitoring 40
2.4.2 Grinding Wheel Topographical Mapping . 41
2.4.3 Wheel Wear Mechanism . 42
2.5 AE-based Monitoring of Face Milling 43
2.6 AE-based Monitoring of Chemical Mechanical Planarization 44
2.6.1 Precision Scribing of CMP-treated Wafers . 45
2.6.2 AE-based Endpoint Detection for CMP . 46
2.7 AE-based Monitoring of Ultraprecision Machining 48
2.7.1 Monitoring of Precision Scribing . 48
2.7.2 Monitoring of Ultraprecision Turning
of Single Crystal Copper 49x Contents
2.7.3 Monitoring of Ultraprecision Turning
of Polycrystalline Copper . 52
2.8 Conclusions . 52
References . 53
3 Tool Condition Monitoring in Machining . 55
Mo A. Elbestawi, Mihaela Dumitrescu and Eu-Gene Ng
3.1 Introduction . 55
3.2 Research Issues . 56
3.2.1 Sensing Techniques 57
3.2.2 Feature Extraction Methods 61
3.2.3 Decision-making Methods 62
3.3 Neural Networks for Tool Condition Monitoring . 63
3.3.1 Structure of MPC Fuzzy Neural Networks . 64
3.3.2 Construction of MPC Fuzzy Neural Networks . 65
3.3.3 Evaluation of MPC Fuzzy Neural Networks 66
3.3.4 Fuzzy Classification and Uncertainties
in Tool Condition Monitoring . 67
3.4 Case Studies 68
3.4.1 Experimental Tests on MPC Fuzzy Neural Networks
for Tool Condition Monitoring . 68
3.4.2 Online Monitoring Technique for the Detection
of Drill Chipping 75
3.5 Conclusions . 78
References . 80
4 Monitoring Systems for Grinding Processes . 83
Bernhard Karpuschewski and Ichiro Inasaki
4.1 Introduction to Grinding Processes . 83
4.2 Need for Monitoring during Grinding . 83
4.3 Monitoring of Process Quantities 84
4.4 Sensors for the Grinding Wheel 91
4.5 Workpiece Sensors 94
4.6 Sensors for Peripheral Systems . 99
4.7 Adaptive Control Systems . 102
4.8 Intelligent Systems for Abrasive Processes . 103
References . 106
5 Condition Monitoring of Rotary Machines . 109
N. Tandon and A. Parey
5.1 Introduction . 109
5.2 Performance Monitoring . 111
5.3 Vibration Monitoring 111
5.3.1 Vibration Signal Processing 118
5.4 Shock Pulse Analysis (SPA) . 124
5.5 Current Monitoring . 125Contents xi
5.6 Acoustic Emission Monitoring . 126
5.7 Wear Debris and Lubricating Oil Analysis . 129
5.7.1 Magnetic Plugs and Chip Detectors 129
5.7.2 Ferrography 129
5.7.3 Particle Counter 132
5.7.4 Spectrographic Oil Analysis (SOA) 133
5.7.5 Lubricating Oil Analysis . 133
5.8 Thermography . 134
5.9 Conclusions . 135
References . 135
6 Advanced Diagnostic and Prognostic Techniques
for Rolling Element Bearings . 137
Thomas R. Kurfess, Scott Billington and Steven Y. Liang
6.1 Introduction . 137
6.2 Measurement Basics . 138
6.3 Bearing Models . 145
6.4 Diagnostics 147
6.4.1 Signal Analysis . 147
6.4.2 Effects of Operating Conditions . 153
6.4.3 Appropriate Use of Fast Fourier Transforms (FFTs) 157
6.4.4 Trending 157
6.5 Prognostics 158
6.6 Conclusions . 163
References . 163
7 Sensor Placement and Signal Processing
for Bearing Condition Monitoring . 167
Robert X. Gao, Ruqiang Yan, Shuangwen Sheng and Li Zhang
7.1 Introduction . 167
7.2 Sensor Placement 169
7.2.1 Structural Attenuation . 169
7.2.2 Simulation of Structural Effects . 171
7.2.3 Experimental Evaluation 173
7.2.4 Sensor Location Ranking 175
7.3 Signal Processing Techniques . 180
7.3.1 Frequency Domain Techniques 180
7.3.2 Time–frequency Techniques . 182
7.3.3 Performance Comparison . 186
7.4 Conclusions . 188
References . 189
8 Monitoring and Diagnosis of Sheet Metal Stamping Processes . 193
R. Du
8.1 Introduction . 193
8.2 A Brief Description of Sheet Metal Stamping Processes 194xii Contents
8.3 Online Monitoring Based on the Tonnage Signal
and Support Vector Regression . 199
8.3.1 A Study of the Tonnage Signal . 199
8.3.2 A Brief Introduction to Support Vector Regression (SVR) 200
8.3.3 Experiment Results . 206
8.3.4 Remarks 207
8.4 Diagnosis Based on Infrared Imaging . 209
8.4.1 A Study of Diagnosis Methods . 209
8.4.2 Thermal Energy and Infrared Imaging . 211
8.5 Conclusions . 215
References . 217
9 Robust State Indicators of Gearboxes Using Adaptive
Parametric Modeling 219
Yimin Zhan and Viliam Makis
9.1 Introduction . 219
9.2 Modeling . 221
9.2.1 Noise-adaptive Kalman Filter-based Model . 221
9.2.2 Bispectral Feature Energy . 224
9.2.3 AR Model Residual-based State Parameter 226
9.2.4 Improved AR Model Residual-based State Parameter . 228
9.3 Experimental Set-up 231
9.4 Performance Analysis of BFE . 233
9.5 Performance Analysis of MRP 235
9.6 Performance Analysis of IMRP 239
9.7 Conclusions . 242
References . 243
10 Signal Processing in Manufacturing Monitoring . 245
C. James Li
10.1 Introduction . 245
10.2 Types of Signatures . 246
10.3 Signal Processing 247
10.3.1 Time Domain Methods . 247
10.3.2 Frequency Domain Methods . 251
10.3.3 Time–frequency Methods . 256
10.3.4 Model-based Methods 260
10.4 Decision-making Strategy . 261
10.4.1 Simple Thresholds 261
10.4.2 Statistical Process Control (SPC) . 262
10.4.3 Time/Position-dependent Thresholds . 262
10.4.4 Part Signature . 262
10.4.5 Waveform Recognition . 263
10.4.6 Pattern Recognition 263
10.4.7 Severity Estimator 263
10.5 Conclusions . 264
References . 264Contents xiii
11 Autonomous Active-sensor Networks for High-accuracy Monitoring
in Manufacturing 267
Ardevan Bakhtari and Beno Benhabib
11.1 Sensor Networks . 267
11.1.1 Sensor Fusion . 268
11.1.2 Sensor Selection . 268
11.1.3 Sensor Modeling . 269
11.1.4 An Example of a Multi-sensor Network . 270
11.2 Active Sensors . 272
11.2.1 Active-sensor Networks for Surveillance of Moving
Objects in Static Environments . 272
11.2.2 Online Sensor Planning for Surveillance
of Dynamic Environments 275
11.3 Agent-based Approach to Online Sensor Planning . 276
11.3.1 Agents . 276
11.3.2 Advantages and Drawbacks of Multi-agent Systems . 277
11.3.3 Examples of Agent-based Sensor-planning Systems 277
11.4 An Active-sensor Network Example for Object Localization
in a Multi-object Environment 282
11.4.1 Experimental Set-up . 282
11.4.2 Experiments 283
References . 286
12 Remote Monitoring and Control in a Distributed Manufacturing
Environment 289
Lihui Wang, Weiming Shen, Peter Orban and Sherman Lang
12.1 Introduction . 289
12.2 WISE-SHOPFLOOR Concept . 290
12.3 Architecture Design 292
12.4 Data Collection and Distribution . 295
12.4.1 Information Flow 295
12.4.2 Applet–Servlet Communication . 295
12.4.3 Sensor Signal Collection and Distribution 296
12.4.4 Virtual Control versus Real Control . 297
12.5 Shop Floor Security 298
12.6 Case Study 1: Remote Robot Control . 299
12.6.1 Constrained Kinematic Model 300
12.6.2 Inverse Kinematic Model . 302
12.6.3 Java 3D Scene-graph Model . 303
12.6.4 Remote Tripod Manipulation 305
12.7 Case Study 2: Remote CNC Machining 307
12.7.1 Test Bed Configuration . 307
12.7.2 Java 3D Visualization . 308
12.7.3 Data Communication 309
12.7.4 Remote Machine Control 309
12.8 Toward Condition-based Monitoring 311xiv Contents
12.9 Conclusions . 312
References . 313
13 An Intelligent Nanofabrication Probe for Surface
Displacement/Profile Measurement . 315
Wei Gao
13.1 Introduction . 315
13.2 Design of the Nanofabrication Probe 317
13.2.1 Concept of the Probe 317
13.2.2 Design of the Probe 320
13.3 Evaluation of the Nanofabrication Probe 327
13.3.1 Evaluation of FTC Performance of the Probe . 327
13.3.2 Evaluation of Force Detection by the Probe . 330
13.3.3 Evaluation of Displacement Detection by the Probe 333
13.4 Nanofabrication and Workpiece Surface Profile Measurement
Using the Probe . 335
13.5 Conclusions . 344
References . 344
14 Smart Transducer Interface Standards for Condition Monitoring
and Control of Machines 347
Kang B. Lee
14.1 Introduction . 347
14.2 IEEE 1451 Smart Transducer Interface Standards 349
14.2.1 IEEE 1451.0 Common Functions and Commands 350
14.2.2 IEEE 1451.1 Networked Smart Transducer Model . 351
14.2.3 IEEE 1451.2 Transducer-to-Microprocessor
Communication Interface . 353
14.2.4 IEEE 1451.3 – Distributed Multi-drop Systems
for Interfacing Smart Transducers 355
14.2.5 IEEE 1451.4 – Mixed-mode Transducer Interface . 356
14.2.6 IEEE P1451.5 – Wireless Transducer Interface 358
14.3 Distributed Control Architecture . 359
14.3.1 Networked Smart Sensor Standards . 360
14.3.2 Network Communications using Ethernet 360
14.3.3 Distributed Measurement and Control Model 361
14.3.4 Web-based Access to Control Network 363
14.3.5 Internet-based Condition Monitoring . 364
14.4 Networked Sensor Application – Machine Tool
Condition Monitoring . 366
14.4.1 Design Approach 369
14.4.2 System Implementation 369
14.4.3 Hardware System Layout . 369
14.5 Conclusions . 370
References . 371Contents xv
15 Rocket Testing and Integrated System Health Management 373
Fernando Figueroa and John Schmalzel
15.1 Introduction . 373
15.2 Background . 375
15.3 ISHM for Rocket Test . 378
15.3.1 Implementation Strategy . 378
15.3.2 DIaK Architecture 378
15.3.3 Object Framework 381
15.4 ISHM Implementation 384
15.4.1 Overall System . 384
15.4.2 Intelligent Sensors 385
15.4.3 Process Models . 388
15.5 Implementation/Validation: Rocket Engine Test Stand 388
15.6 Conclusions and Future Work . 389
References . 390
Index . 393
Index
acoustic emission, 10, 11, 13 14, 19,
33, 35, 37, 39 41, 56 57, 63, 84,
87 89, 109, 111, 126 128, 135,
139, 159 160, 168, 246 247
analysis
cepstrum analysis, 60, 121 122,
151
frequency analysis, 87 88, 115,
128, 226
order analysis, 251
signal analysis, 149, 182
spectral analysis, 123, 181, 189,
220, 251
spectrum analysis, 151, 220, 246,
251
vibration signature analysis, 59
bandwidth, 7, 59, 147, 152, 158, 181,
292, 295, 308, 312, 316, 321,
329, 345, 355
bearing localized defect, 246
bicoherence, 151, 225, 254 255
calibration, 39, 60, 91, 111 112,
269 270, 312, 330, 353 357
capacitance-type, 315, 317, 320 321,
328, 334
CBN, 42, 86 87, 89 90, 92, 97 99,
101
characteristic defect frequency, 254
chatter, 4, 6 8, 10 11, 17, 23, 25, 58,
60, 84, 86 88, 93 94, 312, 369
chip thickness, 35, 39, 41, 53
Choi Williams distribution, 259
classification
fuzzy classification, 62, 65 68
supervised classification, 64, 68,
70, 79
unsupervised classification, 67 68,
79 80
client server, 292, 307, 351
compensation, 14 17, 26, 58,
311 312, 341
compliance, 139, 167
control
adaptive control, 1, 3, 18 20, 22,
36, 57, 83, 94, 102, 106, 312
behavioral control, 291
feedback control, 24, 106, 328, 336,
385
feedforward control, 17
force control, 7, 12, 19 23, 25, 102
measurement control, 360, 370
motion control, 4, 308, 310 311
numerical control, 307, 369
process control, 3 4, 17 19, 23 26,
33, 53, 104, 361
statistical process control, 2
quality control, 2, 26, 53, 106
remote control, 291 292, 296, 311
servo control, 1, 3 5, 15, 23, 25,
317, 319, 333
session control, 292
supervisory control, 1, 4 5, 23 25
control network, 359 361, 375
controller
engine controller, 376
NC controller, 315
open architecture controller, 5, 307
PID controller, 328
convolution theorem, 183
coordination strategy, 280
crest factor, 118 120, 149, 199, 247
cutting tool, 1 3, 6, 9, 11 12, 20, 26,
312, 315 316, 369
cyclostationary, 256394 Index
data acquisition, 40, 60, 113, 168,
200, 206, 232, 348, 358, 370,
376, 379 380
data collection, 292, 296, 307, 309,
312
data packet, 297 298, 305, 309
decision making, 55 58, 60, 62 65,
68, 70, 111, 193, 245, 264, 268,
277, 374
defect propagation, 159 163, 187,
189
deformation, 9, 37, 41, 48, 51, 58,
102, 126 127, 140, 195,
211 212, 214, 311 312
depth of cut, 14, 41, 48, 62, 67, 87,
315 316, 318, 340
design
architecture design, 290, 377
collaborative design, 289, 291, 312
computer-aided design, 23
detection
displacement detection, 333
failure detection, 59, 62, 127
fault detection, 109, 123 124, 225
force detection, 331
state detection, 219, 221, 225
threshold detection, 376
vibration detection, 142
diagnosis, 55, 57, 109, 122 123, 129,
167 168, 180, 193 194, 199,
209, 212, 215 216, 220, 233,
238, 261 262, 291, 348, 373,
375
diagnostics, 118, 120, 137 138, 148,
155, 164, 231, 290, 377
distributed intelligence, 359, 362,
375 376
DMC, 360 361, 363 366
eddy current probe, 140 144 146
effective independence, 167, 175,
189
electrical noise, 321, 324
electrode, 321
electronic data sheet, 112, 347 348,
353, 356
embedded sensing, 171
encoder
rotary encoder, 318 320, 336
surface encoder, 336
energy dissipation, 169
energy loss ratio, 170, 171
energy operator, 248 249
enveloping
bandpass-based enveloping, 181
wavelet-based enveloping, 180,
184, 186 187
error
Abbe error, 321
calibration error, 269 270
cosine error, 321
linearity error, 317, 327
random error, 269 270
statistical error, 269 270
systematic error, 269
event counting, 248
expert system, 26, 62, 111, 132, 272,
275
failure
bearing failure, 137, 148, 164,
167 168
catastrophic failure, 109, 159, 235
fatigue failure, 137
failure mode, 137
feature extraction, 60, 65, 67,
167 168, 263, 268
field network, 349, 360, 370
filtering, 105, 152, 158, 181, 184,
186, 206, 220, 223, 248 249,
251 252, 260, 264, 370
FM0, 253
FM4, 253 254
force
contact force, 315 316, 319, 332,
342
cutting force, 1, 3 4, 6 8, 11, 13 14,
16, 19 22, 36, 48, 56, 60, 62, 67,
260, 263, 308, 311, 316, 369
dynamic force, 168, 172 174, 177,
323
static force, 177
frequency domain, 8, 14, 59, 76, 79,
87, 148, 151, 180, 183, 185 186,
199, 245 247, 251, 264Index 395
frequency domain methods, 251
frequency modulation, 147 148, 248
frequency response, 128, 224, 252,
258, 320, 357
frequency spectrum, 87, 114,
146 147, 183
fusion
decision fusion, 268
feature-level fusion, 268
high-level fusion, 268,272
low-level fusion, 268
mid-level fusion, 268
pixel fusion, 268
sensor fusion, 23, 25, 60, 105, 263,
268 269, 283, 375
gear motion residual, 226, 228, 235,
237, 239, 242
gearbox, 119 120, 122, 219 221, 228,
231 233, 235, 239, 242, 248
health evaluation, 380
health management, 373, 375 377
hysteresis, 317, 327
IEEE 1451, 347 353, 355 361, 364,
366 367, 369 371, 384, 386
infrared imaging, 211
inspection, 2, 11, 34, 163 164, 225–
226, 231 232, 237, 295, 306
workpiece inspection, 245
interferometer, 52, 113, 333 334,
345
interoperability, 348 351, 359 360,
370
ISHM, 373 382, 384 385, 388 389
Java 3D, 291 294, 296, 303 305,
308 309, 311 312
Kalman filter, 1, 219 224, 261
knowledge updating, 57, 62, 64,
67 68, 72 73, 79 80
kurtosis, 60, 76, 118 120, 149 150,
199, 229, 235, 237, 239, 242,
253 254, 261
lead time, 110
learning, 14, 56 57, 60, 62 68, 70,
72, 74, 78 80, 383
life prediction, 164, 168
lock-in amplifier, 331
machine
lathe, 1, 17, 48
milling machine, 259, 307 308, 364
NC machine tool, 2
parallel kinematic machine,
299 300
machinery health, 347
machining
drilling, 1, 3, 6, 15, 23 25, 55,
58 62, 68, 74 76, 78 80
grinding, 1, 3, 6, 19, 33, 36, 39 43,
83 94, 96 104, 106, 339
milling, 1, 3, 6 7, 9, 12, 16, 19 21,
25, 43 44, 61 62, 256, 259,
261, 307 308, 364
NC machining, 311 312
polishing, 45 47
turning, 1, 3, 6 7, 12, 15 16, 19,
34, 49 50, 52, 55, 61 63,
68 69, 74, 79 80, 290,
315 316, 335
diamond turning, 33, 39, 45,
315 317, 324, 335 336, 340
virtual machining, 291, 312
maintenance
condition-based maintenance, 159,
220 221, 347 348
manufacturing
collaborative manufacturing, 298,
312
distributed manufacturing, 289,
290, 292
e-manufacturing, 289, 312
intelligent manufacturing, 373, 375
web-based manufacturing, 290, 307
material
brittle material, 247, 339, 344 345
ductile material, 315, 344
material removal, 1, 35 36, 38, 45,
47, 53, 83 84, 86 88, 93396 Index
measurement
AE measurement, 126 128
displacement measurement, 345
position measurement, 142
profile measurement, 315, 317, 319,
324, 335, 340, 345
MEMS, 33, 112
MIMOSA, 347 349
model
autoregressive model, 219 220
diagnostic model, 160, 162 163,
226
embedded model, 261
Java 3D model, 291 294, 296,
304 305, 309, 311 312
kinematic model, 300, 304 305
Markov model, 264
object model, 349, 351, 378
state pace model, 57
model-based methods, 61
model order selection, 228, 230, 237,
242
modeling
adaptive parametric modeling, 219
finite element modeling, 171, 173
geometric modeling, 308
sensor modelling, 269 270
modulation, 117, 122, 147 148, 245,
252, 254, 315, 331
monitoring
AE monitoring, 43, 127
debris monitoring, 109
health monitoring, 139, 245
in-situ monitoring, 33, 53
online monitoring, 55 56, 58 59,
80
performance monitoring, 111
process monitoring, 6, 10, 18,
25 26, 34 36, 40, 52, 55, 57,
245, 264
real-time monitoring, 289 292,
295 296, 310 312, 348
remote monitoring, 289, 292,
299 300, 304, 306, 309, 347,
364 367
temperature monitoring, 111, 134
tool condition monitoring, 44,
55 57, 60 68, 70, 72, 74 75,
245, 264, 311, 368
vibration monitoring, 109, 219,
221, 242
Monte Carlo simulation, 210
NA4, 253 254
nanofabrication, 315 317, 319 320,
324 327, 329, 331 335,
339 340, 342, 344 345
nanofabrication probe, 317, 319 320,
324 326, 329, 331 325,
339 340, 344 345
NB4, 253 254
NCAP, 348 350, 353 354, 356, 358,
361, 363 364, 366 367,
369 370
neural network, 11, 14, 18, 26, 55,
62, 64 68, 70 73, 78, 80,
88 89, 105, 111, 124, 263
fuzzy neural network, 55, 64 68,
70 72, 74 75, 78 80
nodal displacement, 172, 177 178
nodal-signal-to-noise-ratio, 172, 178
object-oriented framework, 351 352,
361
observation uncertainty, 269
OSA-CBM, 349
part signature, 262
pattern classification, 11, 14, 64, 168,
263
pattern recognition, 62, 63, 111
peak-to-valley, 247, 253
performance index
error covariance matrix, 175 176
Fisher information matrix, 175
photodetector, 132
piezoceramic plate, 173
plug and play, 348, 354, 358, 360, 370
positioning strategy, 280 281
power spectrum, 11, 62, 121 122,
151 152, 180, 186 187, 224,
251 252, 256, 258
probability density function, 63, 248Index 397
process quantity, 89
prognosis, 168, 264, 348, 375
prognostics, 137 138, 159, 163 164,
373, 377
progressive degradation, 374
protocol
communication protocol, 293, 297,
307, 347 350, 353, 357 358,
360
HTTP streaming, 293, 297
TCP, 297, 307, 309, 364
publish subscribe, 292, 309, 312,
351, 362, 364
PZT, 315 317, 319 321, 325 327,
331 333
quartz crystal, 323
rapid prototyping, 289
repeatability, 94, 342
resolution, 40 41, 80, 92, 158, 182,
187, 213, 220, 225, 232, 251,
257, 259, 273 274, 283, 289,
311, 316, 320 321, 324, 331,
333, 336, 345
resonant frequency, 120, 125, 128,
152, 157, 320, 329
response
dynamic response, 327, 329, 345
static response, 327
RMS, 39 41, 43, 45 52, 59, 87, 112,
114 115, 118, 120, 142, 144,
146, 149, 155 156, 162, 229–
230, 232, 234 237, 247, 264
robust state indicator, 219, 221, 242
rocket engine, 373, 375, 378 379,
389
rolling element bearing, 120, 124,
137 138, 164, 189, 248
rule-based reasoning, 382
sampling rate, 40, 114, 120, 158, 233,
296, 306
SB ratio, 253
scene graph, 291 292, 303 304, 308
SEM image, 51
security, 290, 292, 294, 297 299,
307, 309, 311
semiconductor, 33,44, 46, 134
sensing technique, 19, 25, 36, 56, 58,
140, 173, 219
sensitivity, 11, 13, 35 36, 39, 43, 45,
48 49, 91, 128, 158, 164,
172 173, 210 211, 225, 239,
247, 315 317, 331
sensor
accelerometer, 112, 114, 125,
139 140, 142, 144, 146, 149,
155 157, 162 163, 174,
232 234, 356 357
active sensor, 267, 276
AE sensor, 33, 43, 45, 59, 149
competitive sensor, 268 269
complementary sensor, 267
cooperative sensor, 268, 270
displacement sensor, 143, 315, 317,
319 321, 326, 328, 334, 340
force sensor, 315 317, 319, 323,
325, 329 330, 332, 336, 345
intelligent sensor, 291, 377,
383 385, 387 388
microwave sensor, 140 144, 146
piezoelectric sensor, 376
position sensor, 140, 146, 312
shock wave sensor, 173
smart sensor, 112, 347 349, 355,
370, 377 380, 384, 386, 389
physical smart sensor, 384,
386 387
virtual smart sensor, 384, 387
strain gauge, 7, 85
virtual sensor, 261, 264
sensor interface, 347 349, 358
sensor location, 123, 146, 155, 158,
167, 169, 171 180, 186, 189,
195, 273
sensor network, 267 268, 276, 351,
363, 366 367, 369, 385
sensor placement, 156, 167 169, 175,
177, 189
sensor selection, 268 269, 276, 279
severity estimator, 263398 Index
sheet metal stamping, 193 195, 204,
209, 212, 215
signal
AE signal, 14, 37, 39 40, 43, 46–
48, 51 52, 59, 128, 308, 311
difference signal, 43, 253
non-stationary signal, 182, 220, 256
residual signal, 226, 228, 237, 242,
254
time domain signal, 59, 76 77,
119 122, 150, 182
transient signal, 200, 246
vibration signal, 55, 59, 75 76,
112 115, 118 119, 124, 143,
159, 167 171, 175, 180 182,
186 187, 189, 194, 220, 224,
254, 369
signal attenuation, 169, 171, 173
signal processing, 1, 10, 26, 40,
55 58, 69, 74, 79, 109, 118,
120, 124, 128, 137, 148, 151,
158, 160, 167 168, 180, 186,
188,193, 245 247, 252,
255 256, 260, 263 264, 304,
376
short-time signal processing, 256
signal-to-noise ratio, 151 152,
168 169, 175, 177, 188, 205
signature-generating mechanism,
246, 253
sinusoidal, 9, 49, 241, 258, 331,
336 337
socket, 295 297, 309, 364
spectrogram, 256 257
standards
interface standards, 347 348, 350,
364, 369 370
sensor standards, 366, 387
statistical moments, 118, 150, 247,
256
stiffness, 7, 85, 102, 248, 261, 263,
316 317, 320, 324 326, 329
STIM, 353 355, 366 367, 369 370
stress fracture, 374
support vector machine, 194, 216, 263
surface
complex surface, 315 318, 323,
340, 344 345
microstructured surface, 329, 337
surface finish, 7, 9, 13, 19, 26, 34,
36 37, 51 52, 57 58, 150
surface integrity,9, 58, 86, 89, 94, 98
surface roughness, 4, 13, 44, 84, 125,
139
surface temperature, 134
surveillance, 267 269, 272, 274 275,
277 280, 283
synchronized averaging, 250
synthesis, 272 275
system
adaptive control system, 102
agent-based system, 276 277, 280
intelligent grinding system, 83, 105
multi-sensor system, 36, 268
web-based system, 289, 298,
311 312
system configuration, 158, 272, 360,
368
systems-of-systems, 374 375
Taylor factor, 50 52
TEDS, 112, 347 348, 350, 353 360,
369 371
thermal energy distribution, 211 212,
214
thermocouple, 89 91
thermography, 91, 109, 111, 134,
308, 311
threshold, 8, 11, 59, 76, 100, 104, 106
127 128, 132, 142, 150, 160,
205, 209, 248, 261 262, 334,
342, 376
floating threshold, 262
simple threshold, 58, 62
time/position dependent threshold,
262
time domain, 59, 70, 76 77, 79, 87,
89, 118 123, 147, 149 150,
161, 182, 184, 245 247, 258,
264Index 399
time frequency distributions, 124,
187 188, 199, 245 246,
258 259, 263 264
time-synchronous average, 226
time-varying spectrum, 220
tolarance, 311
tool breakage, 8, 10 11, 17, 19,
23 25, 60, 68, 72, 246 247,
263, 369
tool wear, 3 4, 6 7, 9 10, 12 15,
24 25, 36 37, 39, 58, 60, 63,
67 68, 70 72, 74, 246, 248,
259, 261, 263
tracking, 16 17, 21, 36, 250, 268,
270 271, 277 278, 333 334,
370
transducer, 112, 125, 128, 134, 260,
347 349, 351, 353 358,
360 361, 366, 370
smart transducer, 347 352, 360,
362, 364, 369 370
transform
FFT, 59, 76, 78, 114, 156, 158, 185,
220, 251, 256
Fourier transform, 122, 151, 180,
182 185, 225, 251, 254, 258
Discrete Fourier transform, 114
Fast Fourier transform, 59, 114,
220
short-time Fourier transform,
187 188
Hilbert transform, 181, 184,
252 253
wavelet packet transform, 184 185
harmonic wavelet packet
transform, 185 188
wavelet transform, 59, 167, 183–
184, 186, 194, 246, 257 259, 370
continuous wavelet transform, 55,
59, 183
discrete wavelet transform, 59,
183 184
trouble-shooting, 291, 293 294,
311 312
ultraprecision machining, 36, 48, 53
ultrasonic shock, 124
UML, 349, 351, 353
vibration acceleration, 112, 128
vibration spectrum, 115, 117 118
virtual reality, 380, 390
visibility, 272, 278 283
waveform recognition, 263
wear debris, 109, 111, 129 132, 135,
138 139
wear estimation, 13 14, 25
wear mechanism, 43, 129, 131
wear mode, 60, 131
wideband demodulation, 260
Wigner Ville Distribution, 167, 182,
187 188, 258

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